Optimization design of the bearingless switched reluctance motor based on SVM and GA

Author

Xiang Qian-Wen ; Yu-Kun Sun ; Xin-hua Zhang

Author_Institution

Sch. of Inf. & Electr. Eng., Jiangsu Univ., Zhenjiang, China

fYear

2011

fDate

22-24 July 2011

Firstpage

1472

Lastpage

1475

Abstract

The optimization design method of the bearingless switched reluctance motor is presented. This paper mainly aims at nonlinear regression modeling of the bearingless switched reluctance motor with support vector machines, which is based on the finite element method simulating, and parameter optimization of the bearingless switched reluctance motor is based on genetic algorithms. The results prove that the nonparametric model has good precision. More important, the optimized motor can produce rated torque and has the maximum suspension force of every unit of rotor.

Keywords

finite element analysis; genetic algorithms; machine theory; regression analysis; reluctance motors; support vector machines; SVM; bearingless switched reluctance motor; finite element method; genetic algorithm; maximum suspension force; nonlinear regression modeling; nonparametric model; optimization design; parameter optimization; rated torque; rotor; support vector machines; Finite element methods; Frequency modulation; Genetic algorithms; Optimization; Reluctance motors; Support vector machines; Switches; bearingless switched reluctance motor; genetic algorithms optimization; nonparametric modeling; optimization design; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2011 30th Chinese

Conference_Location

Yantai

ISSN

1934-1768

Print_ISBN

978-1-4577-0677-6

Electronic_ISBN

1934-1768

Type

conf

Filename

6000540